Trapline foraging by bumble bees: VII. Adjustments for foraging success following competitor removal
Bibliographic record
Abstract
Animals collecting food from renewable resource patches scattered in space often establish small foraging areas to which they return faithfully. Such area fidelity offers foraging advantages through selection of profitable patches, route minimization, and regular circuit visits to these patches (“trapline foraging”). Resource distribution under field conditions may often vary in time, however, especially when competitors suddenly vanish and a number of patches become available for their neighbors. Previous studies suggested that site-faithful foragers of bumble bees quickly respond to such unexpected events by readjusting their foraging areas, although it is not clear how much their foraging performance was improved, beyond the simple relaxation of competitive pressure, or how they manifest such flexibility while persistently using certain foraging areas or paths. Here, we conducted indoor flight-cage experiments with bumble bees and found that a bee, when encountering a loss of its competitor, improved its foraging performance to a greater extent than expected from a simple relaxation of competitive pressure by increasing the size of its foraging area. Moreover, bees with better-established traplines achieved greater foraging areas after the loss of competitors, suggesting that bees do not necessarily need to “sample” neighboring patches to monitor temporal changes in environments. We discuss how periodical returns and route memory associated with accurate reward values could allow inherently conservative trapliners to make flexible adjustments, by effectively monitoring their circumstances and quickly readjusting to detected changes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".